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""" Test fast_dict.
"""
import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
from sklearn.utils._fast_dict import IntFloatDict, argmin
def test_int_float_dict():
rng = np.random.RandomState(0)
keys = np.unique(rng.randint(100, size=10).astype(np.intp))
values = rng.rand(len(keys))
d = IntFloatDict(keys, values)
for key, value in zip(keys, values):
assert d[key] == value
assert len(d) == len(keys)
d.append(120, 3.0)
assert d[120] == 3.0
assert len(d) == len(keys) + 1
for i in range(2000):
d.append(i + 1000, 4.0)
assert d[1100] == 4.0
def test_int_float_dict_argmin():
# Test the argmin implementation on the IntFloatDict
keys = np.arange(100, dtype=np.intp)
values = np.arange(100, dtype=np.float64)
d = IntFloatDict(keys, values)
assert argmin(d) == (0, 0)
def test_to_arrays():
# Test that an IntFloatDict is converted into arrays
# of keys and values correctly
keys_in = np.array([1, 2, 3], dtype=np.intp)
values_in = np.array([4, 5, 6], dtype=np.float64)
d = IntFloatDict(keys_in, values_in)
keys_out, values_out = d.to_arrays()
assert keys_out.dtype == keys_in.dtype
assert values_in.dtype == values_out.dtype
assert_array_equal(keys_out, keys_in)
assert_allclose(values_out, values_in)
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